Machine Learning Engineer

My client is looking for an experienced ML Infrastructure Engineer to support the deployment, optimisation and scaling of advanced machine learning models in production environments. This role sits at the intersection of research and engineering, focused on ensuring models are reliably transitioned from experimentation through to large-scale deployment.

You will work closely with research and platform teams to build and maintain high-performance inference systems, improve deployment processes and help drive infrastructure improvements that enable faster model iteration and release cycles.

This is a strong opportunity to work on technically complex challenges within a fast-moving and highly collaborative environment.

• Product ionise machine learning models from research through validation, staging and live deployment
• Build, maintain and optimise scalable inference infrastructure supporting high-throughput, low-latency workloads
• Improve performance and reliability across GPU-based environments
• Design and implement model serving and deployment workflows
• Develop monitoring and observability tools to track system performance, errors and utilisation
• Support data preparation and model integration as part of the wider development lifecycle
• Collaborate with research, engineering and infrastructure teams to improve deployment efficiency and platform scalability
• Evaluate and integrate third-party infrastructure and inference tooling where appropriate

• Proven experience deploying and maintaining ML inference systems in production environments
• Strong programming experience in Python and familiarity with modern machine learning frameworks
• Experience working with containerisation and orchestration technologies such as Kubernetes or similar
• Exposure to distributed systems and cloud-based infrastructure
• Experience supporting GPU workloads and performance optimisation
• Strong troubleshooting skills across performance, scaling and system reliability
• Comfortable working cross-functionally within research-led environments
• Ability to operate in fast-paced teams with evolving technical priorities

• Experience building or improving model serving infrastructure
• Understanding of distributed training or inference techniques
• Experience debugging low-level performance or hardware-related issues
• Exposure to real-time or latency-sensitive ML applications